Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy

TL;DR

This paper proposes a model and algorithm for optimizing route prices in mixed autonomous and human-driven traffic networks to reduce congestion and improve social welfare.

Contribution

It introduces a novel model of mixed autonomy traffic flow and a pricing algorithm to incentivize efficient equilibria, supported by theoretical benchmarks.

Findings

The pricing scheme improves traffic flow efficiency.

Theoretical benchmarks can be computed efficiently.

Simulation results demonstrate congestion reduction.

Abstract

Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle platooning and by creating an avenue for influencing people's choice of routes. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies. Developing an algorithm to learn the preferences of the users, we formulate a planning optimization that chooses prices to maximize a social objective. We demonstrate the benefit of the proposed scheme by comparing the results to theoretical benchmarks which we show can be efficiently calculated.